{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Introduction to Data Analytics and Geostatistics and Spatial Data Analytics Courses\n",
    "\n",
    "\n",
    "## GeostatsPy: Variogram Calculation for Subsurface Data Analytics in Python \n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "\n",
    "### PGE 383 Exercise: Basic Univariate Summary Statistics and Data Distribution Representativity Plotting in Python with GeostatsPy\n",
    "\n",
    "Here's a simple workflow with some basic variogram calcuation with irregularly sampled data. This should help you get started with variogram caculation in subsurface modeling.\n",
    "\n",
    "#### Spatial Continuity \n",
    "\n",
    "**Spatial Continuity** is the correlation between values over distance.\n",
    "\n",
    "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n",
    "\n",
    "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n",
    "\n",
    "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n",
    "\n",
    "#### The Semivariogram\n",
    "\n",
    "Function of difference over distance.\n",
    "\n",
    "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "\\end{equation}\n",
    "\n",
    "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n",
    "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "\\end{equation}\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "\\begin{equation}\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Variogram Observations\n",
    "\n",
    "The following are common observations for variograms that should assist with their practical use.\n",
    "\n",
    "##### Observation \\#1 - As distance increases, variability increase (in general).\n",
    "\n",
    "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n",
    "\n",
    "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n",
    "\n",
    "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n",
    "\n",
    "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n",
    "\n",
    "We scan through the entire data set, searching for all possible pair combinations with all other data.  We then calculate the variogram as one half the expectation of squared difference between all pairs.\n",
    "\n",
    "More pairs results in a more reliable measure.\n",
    "\n",
    "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n",
    "\n",
    "**Sill** is the variance, $\\sigma^2_x$\n",
    "\n",
    "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n",
    "\n",
    "we can define the covariance function:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n",
    "\n",
    "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n",
    "\n",
    "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n",
    "\n",
    "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n",
    "\n",
    "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing.  This is known as **nugget effect**.\n",
    "\n",
    "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n",
    "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n",
    "\n",
    "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize experimental semivariograms\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data.csv at https://git.io/fh4gm.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB                       # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                 # GSLIB methods convert to Python    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                               # to set current working directory \n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "import matplotlib.pyplot as plt                         # plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"d:/PGE383\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
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      "text/plain": [
       "       X      Y  Facies  Porosity       Perm           AI\n",
       "0  100.0  900.0     1.0  0.100187   1.363890  5110.699751\n",
       "1  100.0  800.0     0.0  0.107947  12.576845  4671.458560\n",
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"sample_data.csv\")                     # read a .csv file in as a DataFrame\n",
    "#print(df.iloc[0:5,:])                                  # display first 4 samples in the table as a preview\n",
    "df.head()                                               # we could also use this command for a table preview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will work by-facies, that is separating sand and shale facies and working with them separately.  This command extracts the sand and shale 'Facies\" into new DataFrames for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "   index      X      Y  Facies  Porosity       Perm           AI\n",
       "0      1  100.0  800.0     0.0  0.107947  12.576845  4671.458560\n",
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate Sample Data by-facies - Deepcopy\n",
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index() # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n",
    "df_shale.head()                                          # preview the sand only DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics for Tabular Data\n",
    "\n",
    "The table includes X and Y coordinates (meters), Facies 1 and 0 (1 is sandstone and 0 interbedded sand and mudstone), Porosity (fraction), and permeability as Perm (mDarcy). \n",
    "\n",
    "There are a lot of efficient methods to calculate summary statistics from tabular data in DataFrames. The 'describe' command provides count, mean, minimum, maximum, and quartiles all in a nice data table. We use transpose just to flip the table so that features are on the rows and the statistics are on the columns.\n",
    "\n",
    "Let's look at and compare the sumamry statistics for sand and shale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>831.111111</td>\n",
       "      <td>238.857269</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>800.000000</td>\n",
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       "      <th>Y</th>\n",
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       "      <td>100.000000</td>\n",
       "      <td>469.000000</td>\n",
       "      <td>509.000000</td>\n",
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       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>162.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>162.0</td>\n",
       "      <td>0.181001</td>\n",
       "      <td>0.037196</td>\n",
       "      <td>0.083842</td>\n",
       "      <td>0.155735</td>\n",
       "      <td>0.194823</td>\n",
       "      <td>0.207754</td>\n",
       "      <td>0.242298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>162.0</td>\n",
       "      <td>293.798999</td>\n",
       "      <td>399.989890</td>\n",
       "      <td>0.381032</td>\n",
       "      <td>31.996547</td>\n",
       "      <td>155.888123</td>\n",
       "      <td>385.177730</td>\n",
       "      <td>2642.999829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>162.0</td>\n",
       "      <td>3441.701689</td>\n",
       "      <td>969.924695</td>\n",
       "      <td>1844.166880</td>\n",
       "      <td>2748.296631</td>\n",
       "      <td>3179.596509</td>\n",
       "      <td>3998.567914</td>\n",
       "      <td>6197.834381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean         std          min          25%  \\\n",
       "index     162.0   117.981481   59.058816     0.000000    75.250000   \n",
       "X         162.0   831.111111  238.857269    50.000000   800.000000   \n",
       "Y         162.0   526.987654  142.707797   100.000000   469.000000   \n",
       "Facies    162.0     1.000000    0.000000     1.000000     1.000000   \n",
       "Porosity  162.0     0.181001    0.037196     0.083842     0.155735   \n",
       "Perm      162.0   293.798999  399.989890     0.381032    31.996547   \n",
       "AI        162.0  3441.701689  969.924695  1844.166880  2748.296631   \n",
       "\n",
       "                  50%          75%          max  \n",
       "index      117.500000   157.750000   260.000000  \n",
       "X          945.000000   975.000000  1005.000000  \n",
       "Y          509.000000   549.000000   939.000000  \n",
       "Facies       1.000000     1.000000     1.000000  \n",
       "Porosity     0.194823     0.207754     0.242298  \n",
       "Perm       155.888123   385.177730  2642.999829  \n",
       "AI        3179.596509  3998.567914  6197.834381  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sand.describe().transpose()                          # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <td>99.0</td>\n",
       "      <td>149.666667</td>\n",
       "      <td>93.588330</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>225.500000</td>\n",
       "      <td>259.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>99.0</td>\n",
       "      <td>300.444444</td>\n",
       "      <td>196.365820</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>970.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>99.0</td>\n",
       "      <td>425.111111</td>\n",
       "      <td>183.665784</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>376.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>989.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>99.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>99.0</td>\n",
       "      <td>0.100212</td>\n",
       "      <td>0.014483</td>\n",
       "      <td>0.058871</td>\n",
       "      <td>0.091902</td>\n",
       "      <td>0.101987</td>\n",
       "      <td>0.109846</td>\n",
       "      <td>0.141657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>99.0</td>\n",
       "      <td>3.568463</td>\n",
       "      <td>6.782476</td>\n",
       "      <td>0.033611</td>\n",
       "      <td>0.723788</td>\n",
       "      <td>1.530878</td>\n",
       "      <td>3.743835</td>\n",
       "      <td>52.500870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>99.0</td>\n",
       "      <td>5450.493543</td>\n",
       "      <td>728.871517</td>\n",
       "      <td>3595.586977</td>\n",
       "      <td>4897.828718</td>\n",
       "      <td>5570.604405</td>\n",
       "      <td>5951.816775</td>\n",
       "      <td>7881.898531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean         std          min          25%  \\\n",
       "index      99.0   149.666667   93.588330     1.000000    41.500000   \n",
       "X          99.0   300.444444  196.365820    40.000000   201.000000   \n",
       "Y          99.0   425.111111  183.665784    29.000000   376.000000   \n",
       "Facies     99.0     0.000000    0.000000     0.000000     0.000000   \n",
       "Porosity   99.0     0.100212    0.014483     0.058871     0.091902   \n",
       "Perm       99.0     3.568463    6.782476     0.033611     0.723788   \n",
       "AI         99.0  5450.493543  728.871517  3595.586977  4897.828718   \n",
       "\n",
       "                  50%          75%          max  \n",
       "index      201.000000   225.500000   259.000000  \n",
       "X          231.000000   300.000000   970.000000  \n",
       "Y          416.000000   456.000000   989.000000  \n",
       "Facies       0.000000     0.000000     0.000000  \n",
       "Porosity     0.101987     0.109846     0.141657  \n",
       "Perm         1.530878     3.743835    52.500870  \n",
       "AI        5570.604405  5951.816775  7881.898531  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shale.describe().transpose()                         # summary table of shale only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The facies have strong differences in their summary statistics.\n",
    "\n",
    "Let's transform the data grouped overall both facies (sand and shale) and separated by facies to normal score values (Gaussian distributed with a mean of 0.0 and variance of 1.0). This is required for sequential Gaussian simulation (common target for our variogram models) and the Gaussian transform assists with outliers and provides more interpretable variograms. \n",
    "\n",
    "Let's look at the inputs for the GeostatsPy nscore program.  Note the output include an ndarray with the transformed values (in the same order as the input data in Dataframe 'df' and column 'vcol'), and the transformation table in original values and also in normal score values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.nscore(df, vcol, wcol=None, ismooth=False, dfsmooth=None, smcol=0, smwcol=0)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.nscore                                           # see the input parameters required by the nscore function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following command will transform the Porosity and Permeabilty to standard normal. Note: we perform the transform for porsity and permeability and for each for the cases of:\n",
    "\n",
    "1. all facies together\n",
    "2. sand facies only\n",
    "3. shale facies only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform to Gaussian by Facies\n",
    "df['NPor'], tvPor, tnsPor = geostats.nscore(df, 'Porosity') # nscore transform for all facies porosity \n",
    "df_sand['NPor'], tvPorSand, tnsPorSand = geostats.nscore(df_sand, 'Porosity')  # nscore transform for sand facies porosity \n",
    "df_shale['NPor'], tvPorShale, tnsPorShale = geostats.nscore(df_shale, 'Porosity')  # nscore transform for shale facies porosity\n",
    "df['NPerm'], tvPermSand, tnsPermSand = geostats.nscore(df, 'Perm')  # nscore transform for all facies permeability\n",
    "df_sand['NPerm'], tvPermSand, tnsPermSand = geostats.nscore(df_sand, 'Perm')  # nscore transform for sand facies permeability\n",
    "df_shale['NPerm'], tvPermShale, tnsPermShale = geostats.nscore(df_shale, 'Perm')  # nscore transform for shale facies permeability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a previous of one of the DataFrames to make sure that we now have the normal score porosity and permeability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>NPor</th>\n",
       "      <th>NPerm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100187</td>\n",
       "      <td>1.363890</td>\n",
       "      <td>5110.699751</td>\n",
       "      <td>-0.907799</td>\n",
       "      <td>-0.893392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.107947</td>\n",
       "      <td>12.576845</td>\n",
       "      <td>4671.458560</td>\n",
       "      <td>-0.566754</td>\n",
       "      <td>-0.144561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.085357</td>\n",
       "      <td>5.984520</td>\n",
       "      <td>6127.548006</td>\n",
       "      <td>-1.498129</td>\n",
       "      <td>-0.322431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.108460</td>\n",
       "      <td>2.446678</td>\n",
       "      <td>5201.637996</td>\n",
       "      <td>-0.522198</td>\n",
       "      <td>-0.624092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102468</td>\n",
       "      <td>1.952264</td>\n",
       "      <td>3835.270322</td>\n",
       "      <td>-0.810549</td>\n",
       "      <td>-0.758293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       X      Y  Facies  Porosity       Perm           AI      NPor     NPerm\n",
       "0  100.0  900.0     1.0  0.100187   1.363890  5110.699751 -0.907799 -0.893392\n",
       "1  100.0  800.0     0.0  0.107947  12.576845  4671.458560 -0.566754 -0.144561\n",
       "2  100.0  700.0     0.0  0.085357   5.984520  6127.548006 -1.498129 -0.322431\n",
       "3  100.0  600.0     0.0  0.108460   2.446678  5201.637996 -0.522198 -0.624092\n",
       "4  100.0  500.0     0.0  0.102468   1.952264  3835.270322 -0.810549 -0.758293"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                          # preview sand DataFrame with nscore transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks good! One way to check is to see if the relative magnitudes of the normal score transformed values match the original values.  e.g. that the normal score transform of 0.10 porosity normal score is less than the normal score transform of 0.14 porsity.  Also, the normal score transform of values close to the mean value should be close to 0.0 \n",
    "\n",
    "Let's also check the original and transformed sand and shale porosity distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)                                        # plot original sand and shale porosity histograms\n",
    "plt.hist(df_sand['Porosity'], facecolor='red',bins=np.linspace(0.0,0.4,50),alpha=0.2,density=True,edgecolor='black',label='Sand')\n",
    "plt.hist(df_shale['Porosity'], facecolor='blue',bins=np.linspace(0.0,0.4,50),alpha=0.2,density=True,edgecolor='black',label = 'Shale')\n",
    "plt.xlim([0.05,0.25]); plt.ylim([0,35.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Porosity Sand and Shale')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(122)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df_sand['NPor'], facecolor='red',bins=np.linspace(-3.0,3.0,40),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=False,edgecolor='black',label='Sand')\n",
    "plt.hist(df_shale['NPor'], facecolor='blue',bins=np.linspace(-3.0,3.0,40),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=False,edgecolor='black',label='Shale')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,0.50])\n",
    "plt.xlabel('Nscore Porosity'); plt.ylabel('Density'); plt.title('Nscore Porosity Sand and Shale')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal score transform has correctly transformed the porosity over sand and shale facies to standard normal.  Let's plot the location maps of normal score transforms of porosity and permeability for all facies, sand facies and shale facies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.plasma                    # color map\n",
    "plt.subplot(231)\n",
    "GSLIB.locmap_st(df,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - All Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(232)\n",
    "GSLIB.locmap_st(df_sand,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - Sand Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(233)\n",
    "GSLIB.locmap_st(df_shale,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - Shale Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(234)\n",
    "GSLIB.locmap_st(df,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - All Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplot(235)\n",
    "GSLIB.locmap_st(df_sand,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - Sand Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplot(236)\n",
    "GSLIB.locmap_st(df_shale,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - Shale Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.8, wspace=0.4, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what the parameters are for the gamv, irregular data, experimental variogram calculation program."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.gamv(df, xcol, ycol, vcol, tmin, tmax, xlag, xltol, nlag, azm, atol, bandwh, isill)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.gamv                                           # see the input parameters required by the gamv function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the location maps to help determine good variogram calculation parameters.\n",
    "\n",
    "We are ready to calculate variogram! Let's calculate isotropic variograms for the transformed normal score porosity and permeability for sand, shale and mixed (without separating sand and shale).  Some information on the parameters that I chose:\n",
    "\n",
    "```p\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = 0; atol = 90.0; isill = 1\n",
    "```\n",
    "* tmin, tmax are trimming limits - set to have no impact, no need to filter the data\n",
    "* lag_dist, lag_tol are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n",
    "* nlag is number of lags - set to extend just past 50 of the data extent\n",
    "* bandh is the horizontal band width - set to have no effect\n",
    "* azi is the azimuth -  it has not effect since we set atol, the azimuth tolerance, to 90.0\n",
    "* isill is a boolean to standardize the distribution to a variance of 1 - it has no effect since the nscore transform sets the variance to 1.0\n",
    "\n",
    "Let's try running these variograms and visualizing them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate Sample Data Isotropic Variograms\n",
    "# I have included a variety of by-facies and porosity and permeability calculation for you to try out!\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 50.0; lag_tol = 25.0; nlag = 14; bandh = 9999.9; azi = 90; atol = 20.0; isill = 1\n",
    "\n",
    "#lag, por_sand_gamma, por_sand_npair = geostats.gamv(df_sand,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "#lag, por_shale_gamma, por_shale_npair = geostats.gamv(df_shale,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "lag, por_gamma, por_npair = geostats.gamv(df,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "\n",
    "#lag, perm_sand_gamma, perm_sand_npair = geostats.gamv(df_sand,\"X\",\"Y\",\"NPerm\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "#lag, perm_shale_gamma, perm_shale_npair = geostats.gamv(df_shale,\"X\",\"Y\",\"NPerm\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "#lag, perm_gamma, perm_npair = geostats.gamv(df,\"X\",\"Y\",\"NPerm\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(lag,por_gamma,'x',color = 'black',label = 'All')\n",
    "#plt.plot(lag,por_sand_gamma,'x',color = 'orange',label = 'Sand')\n",
    "#plt.plot(lag,por_shale_gamma,'x',color = 'brown',label = 'Shale')\n",
    "plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "plt.title('Isotropic NSCORE Porosity Variogram')\n",
    "plt.xlim([0,700])\n",
    "plt.ylim([0,1.8])\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "#plt.subplot(122)\n",
    "#plt.plot(lag,perm_gamma,'x',color = 'black',label = 'All')\n",
    "#plt.plot(lag,perm_sand_gamma,'x',color = 'orange',label = 'Sand')\n",
    "#plt.plot(lag,perm_shale_gamma,'x',color = 'brown',label = 'Shale')\n",
    "#plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "#plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "#plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "#plt.title('Isotropic NSCORE Permeaiblity Variogram')\n",
    "#plt.xlim([0,700])\n",
    "#plt.ylim([0,1.8])\n",
    "#plt.legend(loc='upper left')\n",
    "#plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The experimental variograms have some interesting features:\n",
    "\n",
    "* the range of the sand porosity and permeability is greater than the shale porosity and permeability range\n",
    "* although the shale short range experimental points may be nosy due to sparce shale data\n",
    "\n",
    "There is much more that you could try out, e.g. direction variograms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I hope you find this code and demonstration useful. I'm always happy to discuss geostatistics, statistical modeling, uncertainty modeling and machine learning,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "**Michael Pyrcz**, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "On Twitter I'm the **GeostatsGuy** and on YouTube my lectures are on the channel, **GeostatsGuy Lectures**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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